var_x <- 1:10
var_y <- 11:20
plot(var_x, var_y)

rand_sample <- sample(var_x, size = 100, replace = TRUE)
hist(rand_sample)

Grammar Of Graphics
- data
- aesthetic mapping
- geometric object
- statistical transformations
- scales
- coordinate system
- position adjustments
- faceting
Load package and data
library(tidyverse)
Registered S3 method overwritten by 'dplyr':
method from
print.rowwise_df
[30m── [1mAttaching packages[22m ─────────────────────────────── tidyverse 1.2.1 ──[39m
[30m[32m✔[30m [34mggplot2[30m 3.2.1 [32m✔[30m [34mpurrr [30m 0.3.2
[32m✔[30m [34mtibble [30m 2.1.3 [32m✔[30m [34mdplyr [30m 0.8.3
[32m✔[30m [34mtidyr [30m 0.8.3 [32m✔[30m [34mstringr[30m 1.4.0
[32m✔[30m [34mreadr [30m 1.3.1 [32m✔[30m [34mforcats[30m 0.4.0[39m
[30m── [1mConflicts[22m ────────────────────────────────── tidyverse_conflicts() ──
[31m✖[30m [34mdplyr[30m::[32mfilter()[30m masks [34mstats[30m::filter()
[31m✖[30m [34mdplyr[30m::[32mlag()[30m masks [34mstats[30m::lag()[39m
data <- read_csv("plot_data.csv")
Parsed with column specification:
cols(
Gene = [31mcol_character()[39m,
Length = [32mcol_double()[39m,
RPF = [32mcol_double()[39m,
mRNA = [32mcol_double()[39m,
uATG = [33mcol_logical()[39m,
Chromosome = [31mcol_character()[39m,
Size = [32mcol_double()[39m
)
data
Structure of ggplot

Simple scatterplot
my_plot +
geom_point()

Changing scales of axes
my_plot +
geom_point() +
scale_x_continuous(trans = "log10") +
scale_y_continuous(trans = "log10")

my_plot +
geom_point() +
scale_x_continuous(trans = "log2") +
scale_y_continuous(trans = "log10")

my_plot +
geom_point() +
scale_x_continuous(trans = "log2")

Specify axis breakpoints
my_plot +
geom_point() +
scale_x_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
scale_y_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000))

Change limits and label styles of plots
my_plot +
geom_point() +
scale_x_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000),
labels = scales::comma) +
scale_y_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000),
labels = scales::comma)

my_plot +
geom_point() +
scale_x_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000),
labels = scales::comma,
limits = c(1000, 25000)) +
scale_y_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000),
labels = scales::comma,
limits = c(1000, 25000))

my_plot +
geom_point() +
scale_x_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000, 25000),
labels = scales::comma,
limits = c(1000, 25000)) +
scale_y_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000, 25000),
labels = scales::comma,
limits = c(1000, 25000))

Axis labels
my_plot +
geom_point() +
scale_x_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
scale_y_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
labs(x = "Yeast mRNA abundance",
y = "Yeast RPF abundance",
title = "Scatterplot")

Changing transparency of points
my_plot +
geom_point(alpha = 0.1) +
scale_x_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
scale_y_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
labs(x = "Yeast mRNA abundance",
y = "Yeast RPF abundance",
title = "Scatterplot")

Color points

Color points with a variables (aesthetic mapping) - continuous scale
ggplot(data = data, aes(x = mRNA, y = RPF, color = Chromosome)) +
geom_point(alpha = 0.4) +
scale_x_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
scale_y_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
labs(x = "Yeast mRNA abundance",
y = "Yeast RPF abundance",
title = "Scatterplot")

ggplot(data = data, aes(x = mRNA, y = RPF, color = uATG)) +
geom_point(alpha = 0.4) +
scale_x_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
scale_y_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
labs(x = "Yeast mRNA abundance",
y = "Yeast RPF abundance",
title = "Scatterplot")

Color points with a variables (aesthetic mapping) - discrete scale and color brewer
ggplot(data = data, aes(x = mRNA, y = RPF, color = uATG)) +
geom_point(alpha = 0.4) +
scale_x_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
scale_y_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
labs(x = "Yeast mRNA abundance",
y = "Yeast RPF abundance",
title = "Scatterplot") +
scale_color_brewer(palette = "Set1")

Change shapes of points
ggplot(data = data, aes(x = mRNA, y = RPF, shape = uATG, color = uATG)) +
geom_point(alpha = 0.4) +
scale_x_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
scale_y_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
labs(x = "Yeast mRNA abundance",
y = "Yeast RPF abundance",
title = "Scatterplot") +
scale_color_brewer(palette = "Set1")

Change sizes of points
ggplot(data = data, aes(x = mRNA, y = RPF, color = uATG)) +
geom_point(alpha = 0.4,
size = 2) +
scale_x_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
scale_y_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
labs(x = "Yeast mRNA abundance",
y = "Yeast RPF abundance",
title = "Scatterplot") +
scale_color_brewer(palette = "Set1")

ggplot(data = data, aes(x = mRNA, y = RPF, color = uATG, size = Size)) +
geom_point(alpha = 0.4) +
scale_x_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
scale_y_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
labs(x = "Yeast mRNA abundance",
y = "Yeast RPF abundance",
title = "Scatterplot") +
scale_color_brewer(palette = "Set1")

ggplot(data = data, aes(x = mRNA, y = RPF, color = Size, shape = uATG)) +
geom_point(alpha = 0.4) +
scale_x_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
scale_y_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
labs(x = "Yeast mRNA abundance",
y = "Yeast RPF abundance",
title = "Scatterplot") +
scale_color_gradient(low = "red", high = "blue")

Adding trendlines and regression lines

Statistical tests
library(ggpubr)
Loading required package: magrittr
Attaching package: ‘magrittr’
The following object is masked from ‘package:purrr’:
set_names
The following object is masked from ‘package:tidyr’:
extract
ggplot(data = data, aes(x = mRNA, y = RPF)) +
geom_point(alpha = 0.4,
size = 2) +
scale_x_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
scale_y_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
labs(x = "Yeast mRNA abundance",
y = "Yeast RPF abundance",
title = "Scatterplot") +
stat_cor() +
geom_smooth(method = "lm")

ggplot(data = data, aes(x = mRNA, y = RPF, color = uATG)) +
geom_point(alpha = 0.4,
size = 2) +
scale_x_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
scale_y_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
labs(x = "Yeast mRNA abundance",
y = "Yeast RPF abundance",
title = "Scatterplot") +
scale_color_brewer(palette = "Set1") +
stat_cor() +
geom_smooth(method = "lm")

ggplot(data = data, aes(x = mRNA, y = RPF, color = Chromosome)) +
geom_point(alpha = 0.4,
size = 2) +
scale_x_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
scale_y_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
labs(x = "Yeast mRNA abundance",
y = "Yeast RPF abundance",
title = "Scatterplot") +
stat_cor() +
geom_smooth(method = "lm")

Separate panels
ggplot(data = data, aes(x = mRNA, y = RPF, color = uATG)) +
geom_point(alpha = 0.4,
size = 2) +
scale_x_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
scale_y_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
labs(x = "Yeast mRNA abundance",
y = "Yeast RPF abundance",
title = "Scatterplot") +
stat_cor() +
geom_smooth(method = "lm") +
facet_grid(.~uATG)

ggplot(data = data, aes(x = mRNA, y = RPF, color = uATG)) +
geom_point(alpha = 0.4,
size = 2) +
scale_x_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
scale_y_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
labs(x = "Yeast mRNA abundance",
y = "Yeast RPF abundance",
title = "Scatterplot") +
stat_cor() +
geom_smooth(method = "lm") +
facet_grid(uATG~.)

ggplot(data = data, aes(x = mRNA, y = RPF, color = uATG)) +
geom_point(alpha = 0.4,
size = 2) +
scale_x_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
scale_y_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
labs(x = "Yeast mRNA abundance",
y = "Yeast RPF abundance",
title = "Scatterplot") +
stat_cor() +
geom_smooth(method = "lm") +
facet_grid(.~Chromosome)

ggplot(data = data, aes(x = mRNA, y = RPF, color = uATG)) +
geom_point(alpha = 0.4,
size = 2) +
scale_x_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
scale_y_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
labs(x = "Yeast mRNA abundance",
y = "Yeast RPF abundance",
title = "Scatterplot") +
stat_cor() +
geom_smooth(method = "lm") +
facet_wrap(.~Chromosome)

Histograms
ggplot(data = data, aes(x = mRNA)) +
geom_histogram() +
scale_x_continuous(trans = "log10") +
labs(x = "Yeast mRNA abundance",
y = "Frequency",
title = "Histogram")

ggplot(data = data, aes(x = mRNA)) +
geom_histogram(bins = 100) +
scale_x_continuous(trans = "log10") +
labs(x = "Yeast mRNA abundance",
y = "Frequency",
title = "Histogram")

ggplot(data = data, aes(x = mRNA, color = uATG)) +
geom_histogram(bins = 100) +
scale_x_continuous(trans = "log10") +
labs(x = "Yeast mRNA abundance",
y = "Frequency",
title = "Histogram")

ggplot(data = data, aes(x = mRNA, color = uATG)) +
geom_histogram(bins = 100, position = "dodge") +
scale_x_continuous(trans = "log10") +
labs(x = "Yeast mRNA abundance",
y = "Frequency",
title = "Histogram")

Density plots
ggplot(data = data, aes(x = mRNA, fill = uATG)) +
geom_density(alpha = 0.5) +
scale_x_continuous(trans = "log10") +
labs(x = "Yeast mRNA abundance",
y = "Frequency",
title = "Density plot") +
scale_fill_brewer(palette = "Set1")

Multi-panel density plots
ggplot(data = data, aes(x = mRNA, fill = uATG)) +
geom_density(alpha = 0.5) +
scale_x_continuous(trans = "log10") +
labs(x = "Yeast mRNA abundance",
y = "Frequency",
title = "Density plot") +
scale_fill_brewer(palette = "Set1") +
facet_wrap(~Chromosome)

Boxplots and Violin plots
ggplot(data = data, aes(x = uATG, y = mRNA, fill = uATG)) +
geom_boxplot() +
scale_y_continuous(trans = "log10") +
labs(x = "uATGs",
y = "Yeast mRNA abundance",
title = "Boxplot") +
scale_fill_brewer(palette = "Set1")

ggplot(data = data, aes(x = uATG, y = mRNA, fill = uATG)) +
geom_boxplot() +
scale_y_continuous(trans = "log10") +
labs(x = "uATGs",
y = "Yeast mRNA abundance",
title = "Boxplot") +
scale_fill_brewer(palette = "Set1") +
stat_compare_means(method = "t.test")

my_comparisons <- list(c("Chr-1", "Chr-2"),
c("Chr-1", "Chr-9"))
ggplot(data = data, aes(x = Chromosome, y = mRNA, fill = Chromosome)) +
geom_boxplot() +
scale_y_continuous(trans = "log10") +
labs(x = "uATGs",
y = "Yeast mRNA abundance",
title = "Boxplot") +
stat_compare_means(comparisons = my_comparisons, method = "t.test")

my_comparisons <- list(c("Chr-1", "Chr-2"),
c("Chr-1", "Chr-9"))
ggplot(data = data, aes(x = Chromosome, y = mRNA, fill = Chromosome)) +
geom_violin() +
scale_y_continuous(trans = "log10") +
labs(x = "uATGs",
y = "Yeast mRNA abundance",
title = "Violin plot") +
stat_compare_means(comparisons = my_comparisons, method = "t.test")

Boxplots with scatter points
ggplot(data = data, aes(x = uATG, y = mRNA, fill = uATG)) +
geom_boxplot() +
scale_y_continuous(trans = "log10") +
labs(x = "uATGs",
y = "Yeast mRNA abundance",
title = "Boxplot") +
scale_fill_brewer(palette = "Set1") +
stat_compare_means(method = "t.test") +
geom_jitter(alpha = 0.3, width = 0.1, height = 0)

Saving plots
myboxplot <- ggplot(data = data, aes(x = uATG, y = mRNA, fill = uATG)) +
geom_boxplot() +
scale_y_continuous(trans = "log10") +
labs(x = "uATGs",
y = "Yeast mRNA abundance",
title = "Boxplot") +
scale_fill_brewer(palette = "Set1") +
stat_compare_means(method = "t.test") +
geom_jitter(alpha = 0.3, width = 0.1, height = 0)
ggsave(myboxplot, filename = "myboxplot.png")
Saving 7 x 7 in image
Interactive plots
myscatterplot <- ggplot(data = data, aes(x = mRNA, y = RPF, color = uATG, label = Gene)) +
geom_point(alpha = 0.4,
size = 2) +
scale_x_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
scale_y_continuous(trans = "log10",
breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
labs(x = "Yeast mRNA abundance",
y = "Yeast RPF abundance",
title = "Scatterplot") +
scale_color_brewer(palette = "Set1") +
stat_cor() +
geom_smooth(method = "lm")
library(plotly)
Registered S3 method overwritten by 'data.table':
method from
print.data.table
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
Attaching package: ‘plotly’
The following object is masked from ‘package:ggplot2’:
last_plot
The following object is masked from ‘package:stats’:
filter
The following object is masked from ‘package:graphics’:
layout
ggplotly(myscatterplot)
Themes
myscatterplot +
theme_pubr()

---
title: "Lab 10 - ggplot"
output: 
  html_notebook: 
    toc: yes
    toc_depth: 4
    toc_float: TRUE
---

```{r}
var_x <- 1:10
var_y <- 11:20

plot(var_x, var_y)

rand_sample <- sample(var_x, size = 100, replace = TRUE)
hist(rand_sample)
```


### Grammar Of Graphics
- data
- aesthetic mapping
- geometric object
- statistical transformations
- scales
- coordinate system
- position adjustments
- faceting

### Load package and data
```{r}
library(tidyverse)
```
```{r}
data <- read_csv("plot_data.csv")
data
```

### Structure of ggplot
```{r}
my_plot <- ggplot(data = data, aes(x = mRNA, y = RPF))
my_plot
```

### Simple scatterplot
```{r}
my_plot + 
  geom_point()
```


### Changing scales of axes
```{r}
my_plot +
  geom_point() +
  scale_x_continuous(trans = "log10") +
  scale_y_continuous(trans = "log10")

my_plot +
  geom_point() +
  scale_x_continuous(trans = "log2") +
  scale_y_continuous(trans = "log10")

my_plot +
  geom_point() +
  scale_x_continuous(trans = "log2")
```


### Specify axis breakpoints
```{r}
my_plot +
  geom_point() +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000))

```



### Change limits and label styles of plots
```{r}
my_plot +
  geom_point() +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000),
                     labels = scales::comma) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000),
                     labels = scales::comma)

my_plot +
  geom_point() +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000),
                     labels = scales::comma,
                     limits = c(1000, 25000)) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000),
                     labels = scales::comma,
                     limits = c(1000, 25000))

my_plot +
  geom_point() +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000, 25000),
                     labels = scales::comma,
                     limits = c(1000, 25000)) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000, 25000),
                     labels = scales::comma,
                     limits = c(1000, 25000))
```

### Axis labels
```{r}
my_plot +
  geom_point() +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  labs(x = "Yeast mRNA abundance", 
       y = "Yeast RPF abundance", 
       title = "Scatterplot")
```


### Changing transparency of points
```{r}
my_plot +
  geom_point(alpha = 0.1) +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  labs(x = "Yeast mRNA abundance", 
       y = "Yeast RPF abundance", 
       title = "Scatterplot")
```


### Color points
```{r}
ggplot(data = data, aes(x = mRNA, y = RPF)) +
  geom_point(alpha = 0.1,
             color = "blue") +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  labs(x = "Yeast mRNA abundance", 
       y = "Yeast RPF abundance", 
       title = "Scatterplot")
```


### Color points with a variables (aesthetic mapping) - continuous scale
```{r}
ggplot(data = data, aes(x = mRNA, y = RPF, color = Chromosome)) +
  geom_point(alpha = 0.4) +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  labs(x = "Yeast mRNA abundance", 
       y = "Yeast RPF abundance", 
       title = "Scatterplot")

ggplot(data = data, aes(x = mRNA, y = RPF, color = uATG)) +
  geom_point(alpha = 0.4) +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  labs(x = "Yeast mRNA abundance", 
       y = "Yeast RPF abundance", 
       title = "Scatterplot")
```


### Color points with a variables (aesthetic mapping) - discrete scale and color brewer
```{r}
ggplot(data = data, aes(x = mRNA, y = RPF, color = uATG)) +
  geom_point(alpha = 0.4) +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  labs(x = "Yeast mRNA abundance", 
       y = "Yeast RPF abundance", 
       title = "Scatterplot") +
  scale_color_brewer(palette = "Set1")

```

### Change shapes of points
```{r}
ggplot(data = data, aes(x = mRNA, y = RPF, shape = uATG, color = uATG)) +
  geom_point(alpha = 0.4) +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  labs(x = "Yeast mRNA abundance", 
       y = "Yeast RPF abundance", 
       title = "Scatterplot") +
  scale_color_brewer(palette = "Set1")
```


### Change sizes of points
```{r}
ggplot(data = data, aes(x = mRNA, y = RPF, color = uATG)) +
  geom_point(alpha = 0.4, 
             size = 2) +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  labs(x = "Yeast mRNA abundance", 
       y = "Yeast RPF abundance", 
       title = "Scatterplot") +
  scale_color_brewer(palette = "Set1")

ggplot(data = data, aes(x = mRNA, y = RPF, color = uATG, size = Size)) +
  geom_point(alpha = 0.4) +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  labs(x = "Yeast mRNA abundance", 
       y = "Yeast RPF abundance", 
       title = "Scatterplot") +
  scale_color_brewer(palette = "Set1")

ggplot(data = data, aes(x = mRNA, y = RPF, color = Size, shape = uATG)) +
  geom_point(alpha = 0.4) +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  labs(x = "Yeast mRNA abundance", 
       y = "Yeast RPF abundance", 
       title = "Scatterplot") +
  scale_color_gradient(low = "red", high = "blue")
```


### Adding trendlines and regression lines
```{r}
ggplot(data = data, aes(x = mRNA, y = RPF)) +
  geom_point(alpha = 0.4, 
             size = 2) +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  labs(x = "Yeast mRNA abundance", 
       y = "Yeast RPF abundance", 
       title = "Scatterplot") +
  geom_smooth(method = "lm",
              color = "red")
```


### Statistical tests
```{r}
library(ggpubr)
```
```{r}
ggplot(data = data, aes(x = mRNA, y = RPF)) +
  geom_point(alpha = 0.4, 
             size = 2) +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  labs(x = "Yeast mRNA abundance", 
       y = "Yeast RPF abundance", 
       title = "Scatterplot") +
  stat_cor() + 
  geom_smooth(method = "lm")

ggplot(data = data, aes(x = mRNA, y = RPF, color = uATG)) +
  geom_point(alpha = 0.4, 
             size = 2) +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  labs(x = "Yeast mRNA abundance", 
       y = "Yeast RPF abundance", 
       title = "Scatterplot") +
  scale_color_brewer(palette = "Set1") +
  stat_cor() +
  geom_smooth(method = "lm")


ggplot(data = data, aes(x = mRNA, y = RPF, color = Chromosome)) +
  geom_point(alpha = 0.4, 
             size = 2) +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  labs(x = "Yeast mRNA abundance", 
       y = "Yeast RPF abundance", 
       title = "Scatterplot") +
  stat_cor() +
  geom_smooth(method = "lm")
```

### Separate panels
```{r}
ggplot(data = data, aes(x = mRNA, y = RPF, color = uATG)) +
  geom_point(alpha = 0.4, 
             size = 2) +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  labs(x = "Yeast mRNA abundance", 
       y = "Yeast RPF abundance", 
       title = "Scatterplot") +
  stat_cor() +
  geom_smooth(method = "lm") +
  facet_grid(.~uATG)

ggplot(data = data, aes(x = mRNA, y = RPF, color = uATG)) +
  geom_point(alpha = 0.4, 
             size = 2) +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  labs(x = "Yeast mRNA abundance", 
       y = "Yeast RPF abundance", 
       title = "Scatterplot") +
  stat_cor() +
  geom_smooth(method = "lm") +
  facet_grid(uATG~.)

ggplot(data = data, aes(x = mRNA, y = RPF, color = uATG)) +
  geom_point(alpha = 0.4, 
             size = 2) +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  labs(x = "Yeast mRNA abundance", 
       y = "Yeast RPF abundance", 
       title = "Scatterplot") +
  stat_cor() +
  geom_smooth(method = "lm") +
  facet_grid(.~Chromosome)

ggplot(data = data, aes(x = mRNA, y = RPF, color = uATG)) +
  geom_point(alpha = 0.4, 
             size = 2) +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  labs(x = "Yeast mRNA abundance", 
       y = "Yeast RPF abundance", 
       title = "Scatterplot") +
  stat_cor() +
  geom_smooth(method = "lm") +
  facet_wrap(.~Chromosome)
```

### Histograms
```{r}
ggplot(data = data, aes(x = mRNA)) +
  geom_histogram() +
  scale_x_continuous(trans = "log10") +
  labs(x = "Yeast mRNA abundance", 
       y = "Frequency", 
       title = "Histogram")

ggplot(data = data, aes(x = mRNA)) +
  geom_histogram(bins = 100) +
  scale_x_continuous(trans = "log10") +
  labs(x = "Yeast mRNA abundance", 
       y = "Frequency", 
       title = "Histogram")

ggplot(data = data, aes(x = mRNA, color = uATG)) +
  geom_histogram(bins = 100) +
  scale_x_continuous(trans = "log10") +
  labs(x = "Yeast mRNA abundance", 
       y = "Frequency", 
       title = "Histogram")

ggplot(data = data, aes(x = mRNA, color = uATG)) +
  geom_histogram(bins = 100, position = "dodge") +
  scale_x_continuous(trans = "log10") +
  labs(x = "Yeast mRNA abundance", 
       y = "Frequency", 
       title = "Histogram")
```


### Density plots
```{r}
ggplot(data = data, aes(x = mRNA, fill = uATG)) +
  geom_density(alpha = 0.5) +
  scale_x_continuous(trans = "log10") +
  labs(x = "Yeast mRNA abundance", 
       y = "Frequency", 
       title = "Density plot") +
  scale_fill_brewer(palette = "Set1")
```



### Multi-panel density plots
```{r}
ggplot(data = data, aes(x = mRNA, fill = uATG)) +
  geom_density(alpha = 0.5) +
  scale_x_continuous(trans = "log10") +
  labs(x = "Yeast mRNA abundance", 
       y = "Frequency", 
       title = "Density plot") +
  scale_fill_brewer(palette = "Set1") +
  facet_wrap(~Chromosome)
```



### Boxplots and Violin plots
```{r}
ggplot(data = data, aes(x = uATG, y = mRNA, fill = uATG)) +
  geom_boxplot() +
  scale_y_continuous(trans = "log10") +
  labs(x = "uATGs", 
       y = "Yeast mRNA abundance", 
       title = "Boxplot") +
  scale_fill_brewer(palette = "Set1")

ggplot(data = data, aes(x = uATG, y = mRNA, fill = uATG)) +
  geom_boxplot() +
  scale_y_continuous(trans = "log10") +
  labs(x = "uATGs", 
       y = "Yeast mRNA abundance", 
       title = "Boxplot") +
  scale_fill_brewer(palette = "Set1") +
  stat_compare_means(method = "t.test")
```

```{r}
my_comparisons <- list(c("Chr-1", "Chr-2"),
                       c("Chr-1", "Chr-9"))

ggplot(data = data, aes(x = Chromosome, y = mRNA, fill = Chromosome)) +
  geom_boxplot() +
  scale_y_continuous(trans = "log10") +
  labs(x = "uATGs", 
       y = "Yeast mRNA abundance", 
       title = "Boxplot") +
  stat_compare_means(comparisons = my_comparisons, method = "t.test")
```

```{r}
my_comparisons <- list(c("Chr-1", "Chr-2"),
                       c("Chr-1", "Chr-9"))

ggplot(data = data, aes(x = Chromosome, y = mRNA, fill = Chromosome)) +
  geom_violin() +
  scale_y_continuous(trans = "log10") +
  labs(x = "uATGs", 
       y = "Yeast mRNA abundance", 
       title = "Violin plot") +
  stat_compare_means(comparisons = my_comparisons, method = "t.test")

```


### Boxplots with scatter points
```{r}
ggplot(data = data, aes(x = uATG, y = mRNA, fill = uATG)) +
  geom_boxplot() +
  scale_y_continuous(trans = "log10") +
  labs(x = "uATGs", 
       y = "Yeast mRNA abundance", 
       title = "Boxplot") +
  scale_fill_brewer(palette = "Set1") +
  stat_compare_means(method = "t.test") +
  geom_jitter(alpha = 0.3, width = 0.1, height = 0)
```


### Saving plots
```{r}
myboxplot <- ggplot(data = data, aes(x = uATG, y = mRNA, fill = uATG)) +
  geom_boxplot() +
  scale_y_continuous(trans = "log10") +
  labs(x = "uATGs", 
       y = "Yeast mRNA abundance", 
       title = "Boxplot") +
  scale_fill_brewer(palette = "Set1") +
  stat_compare_means(method = "t.test") +
  geom_jitter(alpha = 0.3, width = 0.1, height = 0)
```

```{r}
myboxplot
ggsave(myboxplot, filename = "myboxplot.png")
```

### Interactive plots
```{r}
myscatterplot <- ggplot(data = data, aes(x = mRNA, y = RPF, color = uATG, label = Gene)) +
  geom_point(alpha = 0.4, 
             size = 2) +
  scale_x_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  scale_y_continuous(trans = "log10",
                     breaks = c(1, 5, 10, 50, 100, 500, 1000, 5000, 10000)) +
  labs(x = "Yeast mRNA abundance", 
       y = "Yeast RPF abundance", 
       title = "Scatterplot") +
  scale_color_brewer(palette = "Set1") +
  stat_cor() +
  geom_smooth(method = "lm")

```

```{r}
library(plotly)
```

```{r}
ggplotly(myscatterplot)
```

### Themes
```{r}
myscatterplot +
  theme_pubr()
```












